Background modeling from surveillance video via transformed L1 function
Fanlong Zhang, Minghua Wan, Guowei Yang, Zhangjing Yang · 2017
Background modeling from surveillance video plays a key role in event detection and human action recognition. Its goal is finding moving objects in video that are independent of the background scene. Among many background modeling algorithms, robust principle component analysis is a recently popular technique, which characterizes the moving objects via L1norm. However, L1norm often leads to inaccurate solution. To overcome this limiting, this paper proposes a background modeling method based on transformed L1function (BM-TL1). The motivation is that transformed L1function can interpolates L0and L1norms by tuning a parameter. Another merit of the transformed L1function is it enjoys closed form iterative thresholding function, and thus can be optimized efficiently. Experiments demonstrate the effectiveness of the proposed methods.